A Pilot Study Investigating the Use of serum GFAP to Monitor Changes in Brain White Matter Integrity after Repetitive Head Hits During a Single Collegiate football game

Repetitive head hits (RHHs) in sports and military settings are increasingly recognized as a risk factor for adverse neurologic outcomes, but they are not currently tracked. Blood-based biomarkers of concussion have recently been shown to increase after non-concussive RHHs during a single sporting contest, raising the possibility that they could be used in real-time to monitor the brain's early response to repeated asymptomatic head hits. In order to test this hypothesis, we measured GFAP in serum immediately before (T0), immediately after (T1) and 45 minutes (T2) after a single collegiate football game in 30 athletes. GFAP changes were correlated to 3 measures of head impact exposure (number of hits, total linear acceleration, and total rotational acceleration captured by helmet impact sensors) and to changes in brain white matter (WM) integrity, estimated by regional changes in fractional anisotropy (FA) and mean diffusivity (MD) on diffusion tensor imaging from 24 hours before (T-1) to 48 hours after (T3) the game). To account for the potentially confounding effects of physical exertion on GFAP, correlations were adjusted for kilocalories of energy expended during the game measured by wearable body sensors. All 30 participants were male with a mean age of 19.5+1.2 years. No participant had a concussion during the index game. We observed a significant increase in GFAP from T0 to T1 (mean 79.69 vs 91.95 pg/mL, p=0.008) and from T0 to T2 (mean 79.69 vs 99.21 pg/mL, p<0.001). White matter integrity decreased in multiple WM regions but was statistically significant in the right fornix (mean % FA change -1.43, 95% CI:-2.20, -0.66). T0 to T2 increases in GFAP correlated with reduced FA in the left fornix, right fornix, and right medical meniscus, and with increased MD in the right fornix (|r-values| ranged from 0.59-0.61). Adjustment for exertion had minimal effect on these correlations. GFAP changes did not correlate to head hit exposure, but after adjustment for exertion, T0 to T2 increases correlated with all three hit metrics (r-values ranged from 0.69-0.74). Thus, acute elevations in GFAP after a single collegiate football game of RHHs correlated with in-game head hit exposure and with reduced WM integrity 2 days later. These results suggest that GFAP may be a biologically relevant indicator of the brain's early response to RHHs during a single sporting event. Developing tools to measure the neurologic response to RHHs on an individual level has the potential to provide insight into the heterogeneity in adverse outcomes after RHH exposure and for developing effective and personalized countermeasures. Due to small sample size, these findings should be considered preliminary; validation in a larger, independent cohort is necessary.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

Importance

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms.

Objective

To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group.

Design, Setting, and Participants

This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit.

Exposure

AHIs.

Main Outcomes and Measures

Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling.

Results

Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs.

Conclusions and Relevance

In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

Antibody Profiling in COVID-19 Patients with Different Severities by Using Spike Variant Protein Microarrays

The disease progression of COVID-19 varies from mild to severe, even death. However, the link between COVID-19 severities and humoral immune specificities is not clear. Here, we developed a multiplexed spike variant protein microarray (SVPM) and utilized it for quantifying neutralizing activity, drug screening, and profiling humoral immunity. First, we demonstrated the competition between antispike antibody and ACE2 on SVPM for measuring the neutralizing activity against multiple spike variants. Next, we collected the serums from healthy subjects and COVID-19 patients with different severities and profile the neutralizing activity as well as antibody isotypes. We identified the inhibition of ACE2 binding was stronger against multiple variants in severe compared to mild/moderate or critical patients. Moreover, the serum IgG against nonstructural protein 3 was elevated in severe but not in mild/moderate and critical cases. Finally, we evaluated two ACE2 inhibitors, Ramipril and Perindopril, and found the dose-dependent inhibition of ACE2 binding to all the spike variants except for B.1.617.3. Together, the SVPM and the assay procedures provide a tool for profiling neutralizing antibodies, antibody isotypes, and reagent specificities.

Development of SARS-CoV-2 variant protein microarray for profiling humoral immunity in vaccinated subjects

SARS-CoV-2 is quickly evolving from wild-type to many variants and spreading around the globe. Since many people have been vaccinated with various types of vaccines, it is crucial to develop a high throughput platform for measuring the antibody responses and surrogate neutralizing activities against multiple SARS-CoV-2 variants. To meet this need, the present study developed a SARS-CoV-2 variant (CoVariant) array which consists of the extracellular domain of spike variants, e.g., wild-type, D614G, B.1.1.7, B.1.351, P.1, B.1.617, B.1.617.1, B.1.617.2, and B.1.617.3. A surrogate virus neutralization on the CoVariant array was established to quantify the bindings of antibody and host receptor ACE2 simultaneously to spike variants. By using a chimeric anti-spike antibody, we demonstrated a broad binding spectrum of antibodies while inhibiting the bindings of ACE2 to spike variants. To monitor the humoral immunities after vaccination, we collected serums from unvaccinated, partial, or fully vaccinated individuals with either mRNA-1273 or AZD1222 (ChAdOx1). The results showed partial vaccination increased the surrogate neutralization against all the mutants while full vaccination boosted the most. Although IgG, IgA, and IgM isotypes correlated with surrogate neutralizing activities, they behave differently throughout the vaccination processes. Overall, this study developed CoVariant arrays and assays for profiling the humoral responses which are useful for immune assessment, vaccine research, and drug development.

Development and Application of Human Coronavirus Protein Microarray for Specificity Analysis

Coronavirus is an enveloped RNA virus that causes mild to severe respiratory diseases in humans, including HKU1-CoV, 229E-CoV, NL63-CoV, OC43-CoV, SARS-CoV, MERS-CoV, and SARS-CoV-2. Due to the outbreak of SARS-CoV-2, it is important to identify the patients and investigate their immune responses. Protein microarray is one of the best platforms to profile the antibodies in the blood because of its fast, multiplexed, and sensitive nature. To fully understand the immune responses and biological specificities, this study developed a human coronavirus (HCoV) protein microarray and included all seven human coronaviruses and three influenza viruses. Each protein was printed in triplicate and formed 14 identical blocks per array. The HCoV protein microarray showed high reproducibility and sensitivity to the monoclonal antibodies against spike and nucleocapsid protein with detection limits of 10-200 pg. The HCoV proteins that were immobilized on the array were properly folded and functional by showing interactions with a known human receptor, e.g., ACE2. By profiling the serum IgG and IgA from 32 COVID-19 patients and 36 healthy patients, the HCoV protein microarray demonstrated 97% sensitivity and 97% specificity with two biomarkers. The results also showed the cross-reactivity of IgG and IgA in COVID-19 patients to spike proteins from various coronaviruses, including that from SARS-CoV, HKU1-CoV, and OC43-CoV. Finally, an innate immune protein named surfactant protein D showed broad affinities to spike proteins in all human coronaviruses. Overall, the HCoV protein microarray is multiplexed, sensitive, and specific, which is useful in diagnosis, immune assessment, biological development, and drug screening.

Time course and diagnostic utility of NfL, tau, GFAP, and UCH-L1 in subacute and chronic TBI

OBJECTIVE

To determine whether neurofilament light (NfL), glial fibrillary acidic protein (GFAP), tau, and ubiquitin C-terminal hydrolase-L1 (UCH-L1) measured in serum relate to traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) measures of traumatic axonal injury (TAI) in patients with TBI.

METHODS

Patients with TBI (n = 162) and controls (n = 68) were prospectively enrolled between 2011 and 2019. Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days, and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury.

RESULTS

At enrollment, patients with TBI had increased serum NfL compared to controls (p < 0.0001). Serum NfL decreased over the course of 5 years but remained significantly elevated compared to controls. Serum NfL at 30 days distinguished patients with mild, moderate, and severe TBI from controls with an area under the receiver-operating characteristic curve (AUROC) of 0.84, 0.92, and 0.92, respectively. At enrollment, serum GFAP was elevated in patients with TBI compared to controls (p < 0.001). GFAP showed a biphasic release in serum, with levels decreasing during the first 6 months of injury but increasing over the subsequent study visits. The highest AUROC for GFAP was measured at 30 days, distinguishing patients with moderate and severe TBI from controls (both 0.89). Serum tau and UCH-L1 showed weak associations with TBI severity and neuroimaging measures. Longitudinally, serum NfL was the only biomarker that was associated with the likely rate of MRI brain atrophy and DTI measures of progression of TAI.

CONCLUSIONS

Serum NfL shows greater diagnostic and prognostic utility than GFAP, tau, and UCH-L1 for subacute and chronic TBI.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that serum NfL distinguishes patients with mild TBI from healthy controls.

Neurofilament light as a biomarker in traumatic brain injury

OBJECTIVE

To determine whether serum neurofilament light (NfL) correlates with CSF NfL, traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) estimates of traumatic axonal injury (TAI).

METHODS

Participants were prospectively enrolled in Sweden and the United States between 2011 and 2019. The Swedish cohort included 45 hockey players with acute concussion sampled at 6 days, 31 with repetitive concussion with persistent postconcussive symptoms (PCS) assessed with paired CSF and serum (median 1.3 years after concussion), 28 preseason controls, and 14 nonathletic controls. Our second cohort included 230 clinic-based participants (162 with TBI and 68 controls). Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury.

RESULTS

In athletes with paired specimens, CSF NfL and serum NfL were correlated (r = 0.71, p < 0.0001). CSF and serum NfL distinguished players with PCS >1 year from PCS ≤1 year (area under the receiver operating characteristic curve [AUROC] 0.81 and 0.80). The AUROC for PCS >1 year vs preseason controls was 0.97. In the clinic-based cohort, NfL at enrollment distinguished patients with mild from those with moderate and severe TBI (p < 0.001 and p = 0.048). Serum NfL decreased over the course of 5 years (ß = -0.09 log pg/mL, p < 0.0001) but remained significantly elevated compared to controls. Serum NfL correlated with measures of functional outcome, MRI brain atrophy, and DTI estimates of TAI.

CONCLUSIONS

Serum NfL shows promise as a biomarker for acute and repetitive sports-related concussion and patients with subacute and chronic TBI.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that increased concentrations of NfL distinguish patients with TBI from controls.

[Optic nerve morphology and vessel density in eyes with different phases of non-arteritic anterior ischemic optic neuropathy]

Objective: To compare the blood flow around the optic disc and related factors in patients with acute and chronic non-arteritic anterior ischemic optic neuropathy (NAION) and healthy volunteers with small disc cups under the same anatomical structure. Methods: This was a prospective case-control study. NAION patients with unilateral onset and healthy volunteers of the same phase were included in the study conducted at the Department of Ophthalmology of Peking Union Medical College Hospital between February 2017 and September 2018. Patients with a course of ≤ 3 months were categorized in the acute phase of NAION, and those with a course of >3 months were in the chronic phase of NAION. Healthy volunteers were in the control group. All subjects underwent the examination of best corrected visual acuity converted to logarithm of the minimum angle of resolution (LogMAR), measurement of non-contact intraocular pressure, slit lamp examination, small pupil fundus examination, and axial measurement. Optical coherence tomography was used to measure the thickness of retinal nerve fiber layers (RNFL) and retinal ganglion cell complex (GCC). Optical coherence tomography angiography was used to measure the vessel density around the optic disc. NAION patients underwent the visual field examination. Analysis of variance, non-parametric Mann-Whitney U test and Spearman coefficient was used for statistical analysis. Results: This study included 16 patients with acute phase of NAION, aged (57±9) years, 6 males and 10 females. There were 17 patients with chronic disease, aged (56±10) years, 7 males and 10 females. There were 15 healthy controls, aged (57±10) years old, 6 males and 9 females. There were no significant differences in age and gender between the groups (both P>0.05). The RNFL and the GCC in the NAION chronic phase group were significantly thinner than those in the acute phase group [(78±38) μm vs. (191±99) μm, (75±19) μm vs. (98±28) μm; t=4.389, 2.758; both P<0.05]. The cup/disc area ratio, cup/disc vertical diameter ratio and cup/disc horizontal diameter ratio in the chronic phase group were larger than those in the acute phase group [0.18 (0.11, 0.31) vs. 0.05 (0.01, 0.18), 0.45 (0.39, 0.56) vs. 0.22 (0.11, 0.41), 0.39 (0.28, 0.54) vs. 0.20 (0.07, 0.42)], and the difference was statistically significant (U=212.000, 208.000, 205.000; all P<0.05). Compared with the optic disc vessel density in the control group (53%±6%), there was a significant decrease in the acute phase group and the chronic phase group (45%±7%, 41%±8%; t=3.705, 4.940; both P<0.01). The blood vessel density in the nasal inferior of the chronic phase group was significantly lower than that in the acute phase group (36%±8% vs. 42%±7%, P=0.039), other sections didn't have significant difference (all P>0.05). There were tortuous capillaries in 8/16 of the acute phase cases, with a low blood flow density and visual field defect in relative positions. Correlation analysis showed that the whole density and peripapillary density in the NAION patients were negatively correlated with LogMAR, mean visual field defect, cup/disc area ratio, focal loss of volume of GCC and general loss of volume of GCC (r=-0.510, -0.733, -0.372, -0.532, -0.648; all P<0.01), but positively correlated with GCC and RNFL thickness (r=0.604, 0.508; both P<0.01). Conclusions: The optic disc vessel density in the acute phase and chronic phase of NAION is significantly reduced. The vessel density in the nasal area of the chronic phase is significantly reduced compared with the acute phase. The vessel density is correlated with visual acuity, visual field defect, disc indexes, thickness of RNFL and GCC. (Chin J Ophthalmol, 2019, 55: 677-686).

[Vessel density and structure in the macular region of non-arteritic anterior ischemic optic neuropathy patients]

Objective: To explore the correlation between the vessel density and the structure and visual function in patients with non-arteritic anterior ischemic optic neuropathy (NAION) in different stages. Methods: This case-control study included 25 NAION patients (28 eyes)of Peking Union Medical College Hospital from November 2017 to May 2018 and 25 healthy controls(HC) (25 eyes) of matched age and gender. General eye examination, visual field examination, and optical coherence tomography angiography were performed to obtain data of blood flow in the macular area and structure such as ganglion cell complex (GCC) and gross loss of volume (GLV), and focal loss of volume (FLV). All affected eyes were divided into the acute group (≤3 weeks), sub-acute group (4 to 12 weeks), and chronic group (>12 weeks) in line with the course of the disease. The group and regional analyses were made to carry out overall differences of blood flows and structures and the correlations with visual function. Results: There were 25 NAION patients with 28 eyes, 16 males and 9 females, aged (55±9) years. The acute group included 8 patients (8 eyes), and the sub-acute group included 10 patients (10 eyes), while the chronic group comprised 7 patients (10 eyes). The overall macular superficial vessel density of patients with NAION was significantly reduced compared with the HC(42.03%±5.70% vs.49.01%±3.34%, t=-5.546, P<0.01), but the deep vessel density was not significantly reduced (P>0.05). The superficial vessel density of the acute group, sub-acute group, and chronic group was significantly decreased(47.41%±3.51% vs. 41.68%±3.09% vs.38.06%±5.93%, all P<0.05). The GCC thickness in patients with NAION were significantly lower than the HC [(88.5±18.2) μm vs. (102.9±5.4)μm, P<0.05]. The GLV and FLV in patients with NAION were significantly higher than the HC (12.733%±11.216% vs. 0.941%±0.852%, 6.295%±4.291% vs. 0.596%±0.460%, both P<0.05). There was a correlation between the macular superficial vessel density and GCC thickness (r=0.606, P=0.001), FLV(r=-0.552, P=0.002), GLV (r=-0.685, P=0.000) and mean sensitivity (r=0.493, P=0.023). Conclusion: Compared with healthy controls, the macular superficial vessel density in NAION patients decreas along with the course of the disease, and its correlation with structural and visual function is revealed. (Chin J Ophthalmol, 2019, 55:195-202).

Vascular Abnormalities within Normal Appearing Tissue in Chronic Traumatic Brain Injury

Magnetic resonance imaging (MRI) is a powerful tool for visualizing traumatic brain injury(TBI)-related lesions. Trauma-induced encephalomalacia is frequently identified by its hyperintense appearance on fluid-attenuated inversion recovery (FLAIR) sequences. In addition to parenchymal lesions, TBI commonly results in cerebral microvascular injury, but its anatomical relationship to parenchymal encephalomalacia is not well characterized. The current study utilized a multi-modal MRI protocol to assess microstructural tissue integrity (by mean diffusivity [MD] and fractional aniosotropy [FA]) and altered vascular function (by cerebral blood flow [CBF] and cerebral vascular reactivity [CVR]) within regions of visible encephalomalacia and normal appearing tissue in 27 chronic TBI (minimum 6 months post-injury) subjects. Fifteen subjects had visible encephalomalacias whereas 12 did not have evident lesions on MRI. Imaging from 14 age-matched healthy volunteers were used as controls. CBF was assessed by arterial spin labeling (ASL) and CVR by measuring the change in blood-oxygen-level-dependent (BOLD) MRI during a hypercapnia challenge. There was a significant reduction in FA, CBF, and CVR with a complementary increase in MD within regions of FLAIR-visible encephalomalacia (p < 0.05 for all comparisons). In normal-appearing brain regions, only CVR was significantly reduced relative to controls (p < 0.05). These findings indicate that vascular dysfunction represents a TBI endophenotype that is distinct from structural injury detected using conventional MRI, may be present even in the absence of visible structural injury, and persists long after trauma. CVR may serve as a useful diagnostic and pharmacodynamic imaging biomarker of traumatic microvascular injury.

Electro-optically spectrum narrowed, multiline intracavity optical parametric oscillators

We report on the first building of an active spectral narrowing mechanism in a pulsed, multiline optical parametric oscillator (OPO) based on a novel aperiodically poled lithium niobate (APPLN) device constructed using the aperiodic optical superlattice technique. The APPLN device functions simultaneously in the system as a multi-channel optical parametric down converter (OPDC) and an electro-optic (EO) gain spectral filter working on the corresponding (multiple) signal bands. When the APPLN OPO was installed in a diode pumped Nd:YVO₄ laser system, highly narrowed dual-wavelength signal lines (at 1540 and 1550 nm) were observed at the output of the system through EO control of the APPLN. Correspondingly, an enhancement of the power spectral density of the source by a factor of ~7.8 with respect to the system operated in passive mode was found.

Brain Volume, Connectivity, and Neuropsychological Performance in Mild Traumatic Brain Injury: The Impact of Post-Traumatic Stress Disorder Symptoms

Post-traumatic stress disorder (PTSD) is commonly associated with mild traumatic brain injury (mTBI). To better understand their relationship, we examined neuroanatomical structures and neuropsychological performance in a sample of individuals with mTBI, with and without PTSD symptoms. Thirty-nine subjects with mTBI were dichotomized into those with (n = 12) and without (n = 27) significant PTSD symptoms based on scores on the PTSD Checklist. Using a region-of-interest approach, fronto-temporal volumes, fiber bundles obtained by diffusion tensor imaging, and neuropsychological scores were compared between the two groups. After controlling for total intracranial volume and age, subjects with mTBI and PTSD symptoms exhibited volumetric differences in the entorhinal cortex, an area associated with memory networks, relative to mTBI-only patients (F = 4.28; p = 0.046). Additionally, subjects with PTSD symptoms showed reduced white matter integrity in the right cingulum bundle (axial diffusivity, F = 6.04; p = 0.020). Accompanying these structural alterations, mTBI and PTSD subjects also showed impaired performance in encoding (F = 5.98; p = 0.019) and retrieval (F = 7.32; p = 0.010) phases of list learning and in tests of processing speed (Wechsler Adult Intelligence Scale Processing Speed Index, F = 12.23; p = 0.001; Trail Making Test A, F = 5.56; p = 0.024). Increased volume and white matter disruptions in these areas, commonly associated with memory functions, may be related to functional disturbances during cognitively demanding tasks. Differences in brain volume and white matter integrity between mTBI subjects and those with mTBI and co-morbid PTSD symptoms point to neuroanatomical differences that may underlie poorer recovery of mTBI subjects who experience PTSD symptoms. These findings support theoretical models of PTSD and its relationship to learning deficits.

Cerebrospinal fluid Aβ levels correlate with structural brain changes in Parkinson's disease

ParkWest is a large Norwegian multicenter study of newly diagnosed drug-naïve subjects with Parkinson's disease (PD). Cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) from this cohort have significant hippocampal atrophy and ventricular enlargement, compared to normal controls. Here, we aimed to investigate whether the same structural changes are associated with cerebrospinal fluid (CSF) levels of amyloid beta (Aβ)38 , Aβ40 , Aβ42 , total tau (t-tau), and phosphorylated tau (p-tau). We performed three-dimensional radial distance analyses of the hippocampi and lateral ventricles using the MRI data from ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We found significant associations between levels of all three CSF Aβ analytes and t-tau and lateral ventricular enlargement in the pooled sample. In the PDCN sample, all three amyloid analytes showed significant associations with the radial distance of the occipital and frontal horns of the lateral ventricles. CSF Aβ38 and Aβ42 showed negative associations, with enlargement in occipital and frontal horns of the lateral ventricles in the pooled sample, and a negative association with the occipital horns in PDMCI. CSF Aβ levels in early PD correlate with ventricular enlargement, previously associated with PD dementia. Therefore, CSF and MRI markers may help identify PD patients at high risk for developing cognitive decline and dementia in the course of their illness. Contrary to Alzheimer's disease, we found no associations between CSF t-tau and p-tau and hippocampal atrophy.

Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.

Global and regional alterations of hippocampal anatomy in long-term meditation practitioners

Studies linking meditation and brain structure are still relatively sparse, but the hippocampus is consistently implicated as one of the structures altered in meditation practitioners. To explore hippocampal features in the framework of meditation, we analyzed high-resolution structural magnetic resonance imaging data from 30 long-term meditators and 30 controls, closely matched for sex, age, and handedness. Hippocampal formations were manually traced following established protocols. In addition to calculating left and right hippocampal volumes (global measures), regional variations in surface morphology were determined by measuring radial distances from the hippocampal core to spatially matched surface points (local measures). Left and right hippocampal volumes were larger in meditators than in controls, significantly so for the left hippocampus. The presence and direction of this global effect was confirmed locally by mapping the exact spatial locations of the group differences. Altogether, radial distances were larger in meditators compared to controls, with up to 15% difference. These local effects were observed in several hippocampal regions in the left and right hemisphere though achieved significance primarily in the left hippocampal head. Larger hippocampal dimensions in long-term meditators may constitute part of the underlying neurological substrate for cognitive skills, mental capacities, and/or personal traits associated with the practice of meditation. Alternatively, given that meditation positively affects autonomic regulation and immune activity, altered hippocampal dimensions may be one result of meditation-induced stress reduction. However, given the cross-sectional design, the lack of individual stress measures, and the limited resolution of brain data, the exact underlying neuronal mechanisms remain to be established.

Right, left, and center: how does cerebral asymmetry mix with callosal connectivity?

BACKGROUND

Prior research has shown that cerebral asymmetry is associated with differences in corpus callosum connectivity. Such associations were detected in histological and anatomical studies investigating callosal fiber size and density, in neuroimaging investigations based on structural and diffusion tensor imaging, as well as in neuropsychological experiments. However, little is known about typical associations between these factors, and even less about the relative influences of magnitude and direction of cerebral asymmetries. Here, we investigated relationships between callosal connectivity and cerebral asymmetry using precise measures of callosal thickness and selected cerebral structures. We considered both the direction and magnitude of the asymmetries.

METHODS

Associations between cerebral asymmetry and callosal thickness were investigated in 348 cognitively healthy older individuals.

RESULTS

The magnitude and direction of cerebral lateralization were significant independent predictors of callosal thickness. However, associations were small. Leftward asymmetry and increased magnitude of asymmetry were generally associated with increased callosal thickness, mostly in the callosal midbody and isthmus.

CONCLUSIONS

When a large sample of normal individuals is considered, cerebral asymmetries are only subtly associated with callosal thickness.

Hippocampal and ventricular changes in Parkinson's disease mild cognitive impairment

We analyzed T1-weighted brain magnetic resonance imaging data of 100 cognitively normal elderly controls (NC), 127 cognitively normal Parkinson's disease (PD; PDCN) and 31 PD-associated mild cognitive impairment (PDMCI) subjects from the Norwegian ParkWest study. Using automated segmentation methods, followed by the radial distance technique and multiple linear regression we studied the effect of clinical diagnosis on hippocampal and ventricular radial distance while adjusting for age, education, and scanning site. PDCN subjects had significantly smaller bilateral hippocampal radial distance relative to NC. Nonamnestic PDMCI subjects showed smaller right hippocampal radial distance relative to NC. PDMCI subjects showed significant enlargement of all portions of the lateral ventricles relative to NC and significantly larger bilateral temporal and occipital and left frontal lateral ventricular expansion relative to PDCN subjects. Nonamnestic PDMCI subjects showed significant ventricular enlargement spanning all parts of the lateral ventricle while those with amnestic PDMCI showed changes localized to the left occipital horn. Hippocampal atrophy and lateral ventricular enlargement show promise as structural biomarkers for PD.

Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

A nonconservative Lagrangian framework for statistical fluid registration-SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate of strain. In , the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q) . Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

The link between callosal thickness and intelligence in healthy children and adolescents

The link between brain structure and intelligence is a well-investigated topic, but existing analyses have mainly focused on adult samples. Studies in healthy children and adolescents are rare, and normative data specifically addressing the association between corpus callosum morphology and intellectual abilities are quite limited. To advance this field of research, we mapped the correlations between standardized intelligence measures and callosal thickness based on high-resolution magnetic resonance imaging (MRI) data. Our large and well-matched sample included 200 normally developing subjects (100 males, 100 females) ranging from 6 to 17 years of age. Although the strongest correlations were negative and confined to the splenium, the strength and the direction of intelligence-callosal thickness associations varied considerably. While significant correlations in females were mainly positive, significant correlations in males were exclusively negative. However, only the negative correlations in the overall sample (i.e., males and females combined) remained significant when controlling for multiple comparisons. The observed negative correlations between callosal thickness and intelligence in children and adolescents contrast with the positive correlations typically reported in adult samples. However, negative correlations are in line with reports from other pediatric studies relating cognitive measures to other brain attributes such as cortical thickness, gray matter volume, and gray matter density. Altogether, these findings suggest that relationships between callosal morphology and cognition are highly dynamic during brain maturation. Sex effects on links between callosal thickness and intelligence during childhood and adolescence are present but appear rather weak in general.

Boosting power for clinical trials using classifiers based on multiple biomarkers

Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Abeta(42)), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Abeta(42), contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power--a substantial boosting of power relative to standard imaging measures.

Hippocampal, caudate, and ventricular changes in Parkinson's disease with and without dementia

Parkinson's disease (PD) has been associated with mild cognitive impairment (PDMCI) and with dementia (PDD). Using radial distance mapping, we studied the 3D structural and volumetric differences between the hippocampi, caudates, and lateral ventricles in 20 cognitively normal elderly (NC), 12 cognitively normal PD (PDND), 8 PDMCI, and 15 PDD subjects and examined the associations between these structures and Unified Parkinson's Disease Rating Scale (UPDRS) Part III:motor subscale and Mini-Mental State Examination (MMSE) performance. There were no hippocampal differences between the groups. 3D caudate statistical maps demonstrated significant left medial and lateral and right medial atrophy in the PDD vs. NC, and right medial and lateral caudate atrophy in PDD vs. PDND. PDMCI showed trend-level significant left lateral caudate atrophy vs. NC. Both left and right ventricles were significantly larger in PDD relative to the NC and PDND with posterior (body/occipital horn) predominance. The magnitude of regionally significant between-group differences in radial distance ranged between 20-30% for caudate and 5-20% for ventricles. UPDRS Part III:motor subscale score correlated with ventricular enlargement. MMSE showed significant correlation with expansion of the posterior lateral ventricles and trend-level significant correlation with caudate head atrophy. Cognitive decline in PD is associated with anterior caudate atrophy and ventricular enlargement.

3D mapping of brain differences in native signing congenitally and prelingually deaf subjects

In the prelingual and congenital deaf, functional reorganization is known to occur throughout brain regions normally associated with hearing. However, the anatomical correlates of these changes are not yet well understood. Here, we perform the first tensor-based morphometric analysis of voxel-wise volumetric differences in native signing prelingual and congenitally deaf subjects when compared with hearing controls. We obtained T1-weighted scans for 14 native signing prelingual and congenitally deaf subjects and 16 age- and gender-matched controls. We used linear and fluid registration to align each image to a common template. Using the voxel-wise determinant of the Jacobian of the fluid deformation, significant volume increases, of up to 20%, were found in frontal lobe white matter regions including Broca's area, and adjacent regions involved in motor control and language production. A similar analysis was performed on hand-traced corpora callosa. A strong trend for group differences was found in the area of the splenium considered to carry fibers connecting the temporal (and occipital) lobes. These anatomical differences may reflect experience-mediated developmental differences in myelination and cortical maturation associated with prolonged monomodal sensory deprivation.

Tensor-based analysis of genetic influences on brain integrity using DTI in 100 twins

Information from the full diffusion tensor (DT) was used to compute voxel-wise genetic contributions to brain fiber microstructure. First, we designed a new multivariate intraclass correlation formula in the log-Euclidean framework. We then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (SEM) that computes the variance contributions in the DTs from genetic (A), common environmental (C) and unique environmental (E) factors. Our algorithm was tested on DT images from 25 identical and 25 fraternal twin pairs. After linear and fluid registration to a mean template, we computed the intraclass correlation and Falconer's heritability statistic for several scalar DT-derived measures and for the full multivariate tensors. Covariance matrices were found from the DTs, and inputted into SEM. Analyzing the full DT enhanced the detection of A and C effects. This approach should empower imaging genetics studies that use DTI.

VENTRICULAR MAPS IN 804 SUBJECTS CORRELATE WITH COGNITIVE DECLINE, CSF PATHOLOGY, AND IMMINENT ALZHEIMER'S DISEASE

There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to predict future decline, and should be computable automatically from hundreds to thousands of images without user intervention. Here we used our multi-atlas fluid image alignment method (MAFIA [1]), to automatically segment parametric 3D surface models of the lateral ventricles in brain MRI scans from 184 AD, 391 MCI, and 229 healthy elderly controls. Radial expansion of the ventricles, computed pointwise, was correlated with measures of (1) clinical decline, (2) pathology from CSF, and (3) future deterioration. Surface-based correlation maps were assessed using a cumulative distribution function method to rank influential covariates according to their effect sizes. The resulting approach is highly automated, and boosts the power of fluid image registration by integrating multiple independent registrations to reduce segmentation errors.

STATISTICALLY ASSISTED FLUID IMAGE REGISTRATION ALGORITHM - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure-applied to 46 3D brain scans from healthy monozygotic twins.

Brain structure changes visualized in early- and late-onset blind subjects

We examined 3D patterns of volume differences in the brain associated with blindness, in subjects grouped according to early and late onset. Using tensor-based morphometry, we mapped volume reductions and gains in 16 early-onset (EB) and 16 late-onset (LB) blind adults (onset <5 and >14 years old, respectively) relative to 16 matched sighted controls. Each subject's structural MRI was fluidly registered to a common template. Anatomical differences between groups were mapped based on statistical analysis of the resulting deformation fields revealing profound deficits in primary and secondary visual cortices for both blind groups. Regions outside the occipital lobe showed significant hypertrophy, suggesting widespread compensatory adaptations. EBs but not LBs showed deficits in the splenium and the isthmus. Gains in the non-occipital white matter were more widespread in the EBs. These differences may reflect regional alterations in late neurodevelopmental processes, such as myelination, that continue into adulthood.

Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

Pattern of hippocampal shape and volume differences in blind subjects

Numerous studies in animals and humans have shown that the hippocampus (HP) is involved in spatial navigation and memory. Blind subjects, in particular, must memorize extensive information to compensate for their lack of immediate updating of spatial information. Increased demands on spatial cognition and memory may be associated with functional and structural HP plasticity. Here we examined local size and shape differences in the HP of blind and sighted individuals. A 3D parametric mesh surface was generated to represent right and left HPs in each individual, based on manual segmentations of 3D volumetric T1-weighted MR images of 22 blind subjects and 28 matched controls. Using a new surface mapping algorithm described in (Shi, Y., Thompson, P.M., de Zubicaray, G.I., Rose, S.E., Tu, Z., Dinov, I., Toga, A.W., Direct mapping of hippocampal surfaces with intrinsic shape context, NeuroImage, Available online May 24, (In Press).), we created an average hippocampal surface for the controls, and computed its normal distance to each individual surface. Statistical maps were created to visualize systematic anatomical differences between groups, and randomization tests were performed to correct for multiple comparisons. In both scaled and unscaled data, the anterior right HP was significantly larger, and the posterior right HP significantly smaller in blind individuals. No significant differences were found for left HP. These differences may reflect adaptive responses to sensory deprivation, and/or increased functional demands on memory systems. They offer a neuroanatomical substrate for future correlations with measures of navigation performance or functional activations related to variations in cognitive strategies.

A LAGRANGIAN FORMULATION FOR STATISTICAL FLUID REGISTRATION

We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithm developed in [4], and used a Lagrangian framework to incorporate 0 th and 1 rst order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensor-based distances to compare the power and the robustness of the algorithms.

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls

We aimed to improve on the single-atlas ventricular segmentation method of (Carmichael, O.T., Thompson, P.M., Dutton, R.A., Lu, A., Lee, S.E., Lee, J.Y., Kuller, L.H., Lopez, O.L., Aizenstein, H.J., Meltzer, C.C., Liu, Y., Toga, A.W., Becker, J.T., 2006. Mapping ventricular changes related to dementia and mild cognitive impairment in a large community-based cohort. IEEE ISBI. 315-318) by using multi-atlas segmentation, which has been shown to lead to more accurate segmentations (Chou, Y., Leporé, N., de Zubicaray, G., Carmichael, O., Becker, J., Toga, A., Thompson, P., 2008. Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease, NeuroImage 40(2): 615-630); with this method, we calculated minimal numbers of subjects needed to detect correlations between clinical scores and ventricular maps. We also assessed correlations between emerging CSF biomarkers of Alzheimer's disease pathology and localizable deficits in the brain, in 80 AD, 80 mild cognitive impairment (MCI), and 80 healthy controls from the Alzheimer's Disease Neuroimaging Initiative. Six expertly segmented images and their embedded parametric mesh surfaces were fluidly registered to each brain; segmentations were averaged within subjects to reduce errors. Surface-based statistical maps revealed powerful correlations between surface morphology and 4 variables: (1) diagnosis, (2) depression severity, (3) cognitive function at baseline, and (4) future cognitive decline over the following year. Cognitive function was assessed using the mini-mental state exam (MMSE), global and sum-of-boxes clinical dementia rating (CDR) scores, at baseline and 1-year follow-up. Lower CSF Abeta(1-42) protein levels, a biomarker of AD pathology assessed in 138 of the 240 subjects, were correlated with lateral ventricular expansion. Using false discovery rate (FDR) methods, 40 and 120 subjects, respectively, were needed to discriminate AD and MCI from normal groups. 120 subjects were required to detect correlations between ventricular enlargement and MMSE, global CDR, sum-of-boxes CDR and clinical depression scores. Ventricular expansion maps correlate with pathological and cognitive measures in AD, and may be useful in future imaging-based clinical trials.

REDUCING STRUCTURAL VARIATION TO DETERMINE THE GENETICS OF WHITE MATTER INTEGRITY ACROSS HEMISPHERES - A DTI STUDY OF 100 TWINS

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.

THE MULTIVARIATE A/C/E MODEL AND THE GENETICS OF FIBER ARCHITECTURE

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 pairs of monozygotic (MZ) twins and 25 pairs of dizygotic (DZ) twins. First, the structural and DT scans were linearly co-registered. The structural MR scans were nonlinear mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was found between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quantitative genetic studies can make use of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Mapping the regional influence of genetics on brain structure variability--a tensor-based morphometry study

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8+/-1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

Mapping genetic influences on ventricular structure in twins

Despite substantial progress in measuring the anatomical and functional variability of the human brain, little is known about the genetic and environmental causes of these variations. Here we developed an automated system to visualize genetic and environmental effects on brain structure in large brain MRI databases. We applied our multi-template segmentation approach termed "Multi-Atlas Fluid Image Alignment" to fluidly propagate hand-labeled parameterized surface meshes, labeling the lateral ventricles, in 3D volumetric MRI scans of 76 identical (monozygotic, MZ) twins (38 pairs; mean age=24.6 (SD=1.7)); and 56 same-sex fraternal (dizygotic, DZ) twins (28 pairs; mean age=23.0 (SD=1.8)), scanned as part of a 5-year research study that will eventually study over 1000 subjects. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps, derived from path analysis, revealed patterns of heritability, and their significance, in 3D. Path coefficients for the 'ACE' model that best fitted the data indicated significant contributions from genetic factors (A=7.3%), common environment (C=38.9%) and unique environment (E=53.8%) to lateral ventricular volume. Earlier-maturing occipital horn regions may also be more genetically influenced than later-maturing frontal regions. Maps visualized spatially-varying profiles of environmental versus genetic influences. The approach shows promise for automatically measuring gene-environment effects in large image databases.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

A NEW REGISTRATION METHOD BASED ON LOG-EUCLIDEAN TENSOR METRICS AND ITS APPLICATION TO GENETIC STUDIES

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation (or strain) tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

COMPARISON OF FRACTIONAL AND GEODESIC ANISOTROPY IN DIFFUSION TENSOR IMAGES OF 90 MONOZYGOTIC AND DIZYGOTIC TWINS

We used diffusion tensor magnetic resonance imaging (DTI) to reveal the extent of genetic effects on brain fiber microstructure, based on tensor-derived measures, in 22 pairs of monozygotic (MZ) twins and 23 pairs of dizygotic (DZ) twins (90 scans). After Log-Euclidean denoising to remove rank-deficient tensors, DTI volumes were fluidly registered by high-dimensional mapping of co-registered MP-RAGE scans to a geometrically-centered mean neuroanatomical template. After tensor reorientation using the strain of the 3D fluid transformation, we computed two widely-used scalar measures of fiber integrity: the fractional anisotropy (FA), and geodesic anisotropy (GA), which measures the geodesic distance between tensors in the symmetric positive-definite tensor manifold. Spatial maps of intraclass correlations (r) between MZ and DZ twins were compared to compute maps of Falconer's heritability statistics, i.e. the proportion of population variance explainable by genetic differences among individuals. Cumulative distribution plots (CDF) of effect sizes showed that the manifold measure, GA, marginally outperformed the Euclidean measure, FA, in detecting genetic correlations. While maps were relatively noisy, the CDFs showed promise for detecting genetic influences on brain fiber integrity as the current sample expands.

BEST INDIVIDUAL TEMPLATE SELECTION FROM DEFORMATION TENSOR MINIMIZATION

We study the influence of the choice of template in tensor-based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on average than random pairings, consistent with evidence that brain structure is under strong genetic control. We also computed the intraclass correlation and associated permutation p-value at each voxel for the determinant of the Jacobian matrix of the transformation. The cumulative distribution function (cdf) of the p-values was found at each voxel for each of the templates and compared to the null distribution. Surprisingly, there was very little difference between CDFs of statistics computed from analyses using different templates. As the brain with least log-Euclidean deformation cost, the mean template defined here avoids the blurring caused by creating a synthetic image from a population, and when selected from a large population, avoids bias by being geometrically centered, in a metric that is sensitive enough to anatomical similarity that it can even detect genetic affinity among anatomies.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

Fast 3D Fluid Registration of Brain Magnetic Resonance Images

Fluid registration is widely used in medical imaging to track anatomical changes, to correct image distortions, and to integrate multi-modality data. Fluid mappings guarantee that the template image deforms smoothly into the target, without tearing or folding, even when large deformations are required for accurate matching. Here we implemented an intensity-based fluid registration algorithm,⁷ accelerated by using a filter designed by Bro-Nielsen and Gramkow.³ We validated the algorithm on 2D and 3D geometric phantoms using the mean square difference between the final registered image and target as a measure of the accuracy of the registration. In tests on phantom images with different levels of overlap, varying amounts of Gaussian noise, and different intensity gradients, the fluid method outperformed a more commonly used elastic registration method, both in terms of accuracy and in avoiding topological errors during deformation. We also studied the effect of varying the viscosity coefficients in the viscous fluid equation, to optimize registration accuracy. Finally, we applied the fluid registration algorithm to a dataset of 2D binary corpus callosum images and 3D volumetric brain MRIs from 14 healthy individuals to assess its accuracy and robustness.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors

This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling's $T(2) test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulative p-value plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and large-scale studies of disease that use tensor-based morphometry.

Giant enhancement of band edge emission based on ZnO/TiO(2) nanocomposites

Enhancement of band edge emission of ZnO nanorods up to a factor of 120 times has been observed in the composite consisting of ZnO nanorods and TiO(2) nanoparticles, while the defect emission of ZnO nanorods is quenched to noise level. Through a detailed investigation, it is found that the large enhancement mainly arises from fluorescence resonance energy transfer between the band edge transition of ZnO nanorods and TiO(2) nanoparticles. Our finding opens up new possibilities for the creation of highly efficient solid state emitters.

Mapping Genetic Influences on Brain Shape using Multi-Atlas Fluid Image Alignment

In this pilot study, we developed a set of computer vision based surface segmentation and statistical shape analysis algorithms to study genetic influences on brain structure in a database of brain MRI scans of normal twins. A set of manually delineated 3D parametric surfaces, representing the lateral ventricles, was deformed, using a Navier-Stokes fluid image registration algorithm, onto all the images in the database. The geometric transformations thus obtained were used to propagate the segmentation labels to all the other images. 3D radial distance maps were derived to encode anatomical shape differences. The proportion of shape variance attributable to genetic factors, known as the heritability, was estimated from the shape models using a restricted maximum likelihood method to increase statistical power. Segmentation errors associated with projecting labels onto new images were greatly reduced through multi-atlas averaging. The resulting algorithms provide a convenient and sensitive tool to recover and analyze small intra-pair image differences, and will make it easier to detect genetic influences on brain structure.

Multivariate statistics of the Jacobian matrices in tensor based morphometry and their application to HIV/AIDS

Tensor-based morphometry (TBM) is widely used in computational anatomy as a means to understand shape variation between structural brain images. A 3D nonlinear registration technique is typically used to align all brain images to a common neuroanatomical template, and the deformation fields are analyzed statistically to identify group differences in anatomy. However, the differences are usually computed solely from the determinants of the Jacobian matrices that are associated with the deformation fields computed by the registration procedure. Thus, much of the information contained within those matrices gets thrown out in the process. Only the magnitude of the expansions or contractions is examined, while the anisotropy and directional components of the changes are ignored. Here we remedy this problem by computing multivariate shape change statistics using the strain matrices. As the latter do not form a vector space, means and covariances are computed on the manifold of positive-definite matrices to which they belong. We study the brain morphology of 26 HIV/AIDS patients and 14 matched healthy control subjects using our method. The images are registered using a high-dimensional 3D fluid registration algorithm, which optimizes the Jensen-Rényi divergence, an information-theoretic measure of image correspondence. The anisotropy of the deformation is then computed. We apply a manifold version of Hotelling's T2 test to the strain matrices. Our results complement those found from the determinants of the Jacobians alone and provide greater power in detecting group differences in brain structure.

Enhancement of band gap emission stimulated by defect loss

Defect radiation has been always considered as the most important loss for an emitter based on band gap emission. Here, we propose a novel approach which goes against this conventional wisdom. Based on the resonance effect between the surface plasmon of metal nanoparticles and defect emission, it is possible to convert the useless defect radiation to the useful excitonic emission with a giant enhancement factor. Through the transfer of the energetic electrons excited by surface plasmon from metal nanoparticles to the conduction band of the emitter, the band gap emission can be greatly enhanced, while the defect emission can be suppressed to noise level.